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   "source": [
    "## 逻辑回归预测芯片质量\n",
    "### 1、基于chip_test.csv数据，建立逻辑回归模型（二阶边界），评估模型表现\n",
    "### 2、以函数的方式求解边界曲线\n",
    "### 3、描绘出完整的决策边界曲线"
   ],
   "cell_type": "markdown",
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  },
  {
   "cell_type": "code",
   "execution_count": 59,
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   "outputs": [
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     "data": {
      "text/plain": "      test1    test2  pass\n0  0.051267  0.69956     1\n1 -0.092742  0.68494     1\n2 -0.213710  0.69225     1\n3 -0.375000  0.50219     1\n4  0.183760  0.93348     0",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>test1</th>\n      <th>test2</th>\n      <th>pass</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.051267</td>\n      <td>0.69956</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.092742</td>\n      <td>0.68494</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-0.213710</td>\n      <td>0.69225</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.375000</td>\n      <td>0.50219</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.183760</td>\n      <td>0.93348</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 59
    }
   ],
   "source": [
    "# 加载数据\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv('chip_test.csv')\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 可视化数据\n",
    "from matplotlib import pyplot as plt\n",
    "mask = data.loc[:, 'pass'] == 1\n",
    "fig1 = plt.figure()\n",
    "passed = plt.scatter(data.loc[:, 'test1'][mask], data.loc[:, 'test2'][mask])\n",
    "failed = plt.scatter(data.loc[:, 'test1'][~mask], data.loc[:, 'test2'][~mask])\n",
    "plt.title('test1-test2')\n",
    "plt.xlabel('test1')\n",
    "plt.ylabel('test2')\n",
    "plt.legend((passed, failed), ('passed', 'failed'))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 生成新数据，定义x，y\n",
    "X = data.drop('pass', axis=1)\n",
    "y = data.loc[:, 'pass']\n",
    "X1 = data.loc[:, 'test1']\n",
    "X2 = data.loc[:, 'test2']\n",
    "X1_2 = X1 * X1\n",
    "X2_2 = X2 * X2\n",
    "X1_X2 = X1 * X2\n",
    "X_new = {'X1': X1, 'X2': X2, 'X1_2': X1_2, 'X2_2': X2_2, 'X1_X2': X1_X2}\n",
    "X_new = pd.DataFrame(X_new)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "LogisticRegression()"
     },
     "metadata": {},
     "execution_count": 62
    }
   ],
   "source": [
    "# 建立模型并训练\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "LR = LogisticRegression()\n",
    "LR.fit(X_new, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.8135593220338984"
     },
     "metadata": {},
     "execution_count": 63
    }
   ],
   "source": [
    "# 预测结果并评估表现\n",
    "from sklearn.metrics import accuracy_score\n",
    "y_predict = LR.predict(X_new)\n",
    "accuracy = accuracy_score(y, y_predict)\n",
    "accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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UAYei5WkxweRGH8Nt5/c6YuQUpFZrcDW1vJMd0enYqZ0BQ4FNiG9NWCKSCzwMDAf6AGNEpE/kMap6v6oOVNWBwB3AO6q6M+KQs8P7LXikETf6GEYP6sy9l/Sjc2EBAnQuLODeS/oFc9SUkx3R6dipbSssZgw/ayBDgNWqugZARJ4BRgGfxjh+DPB3j8pmXOR0baFG2ixI5WRSSzcSZLrNyVqTNYX5ys9O9M5AZPrMkvC2OkSkKXAB8FzEZgVmicgCERkX60lEZJyIzBeR+TbzNBjSqrbgBic7otOxU9upWpPXAwhMHb6tByIilwLnq+p14ftXA0NU9ftRjv0OcJWqjojY1klVN4rI0cAbwPdVdU59z5n164EYEwS1+0AgVGtKNPC5sb6NiSqI64GUAF0i7hcBG2Mcezm1mq9UdWP491YReYFQk1i9ASRruFmtD2CTQdDyVgWtPIGTYObcmNJxAEGG8TOAzAN6ikgPYAOhIHFF7YNEpCVwJnBVxLZmQI6q7g3fPg+wHjhwd4SLz6Nnon0xA4HKW5WOebR8CXhODAV2cxEpExff+kBU9RAwAZgJLAemq+oyERkvIuMjDr0YmKWq+yK2tQfeE5FPgI+A11T1X16VPWVuzhx2c4SLj6NnYk0+/Nkry5Kage70TPgaTs6I94Ibkzo9Y7PifefrPBBVnQHMqLXt0Vr3nwSerLVtDTDA5eK5w+2reDer9T42GcT6Yq69rUZ9c0rcrCWkWx4tpyd1elqbcaopzCTNZqJ7ze2Zw25W631sMkj0C7i+OSVuZtt1cka8F5wMeL403wVhhc0A9gt6xXJhec3tq3g3q/U+NhnE+gIuLMhLOG+Vm7UE1/NoOczJSZ3p1nzniCwfSmwBxGtuzxx2c16Aj3MOYn0xTxp5At86qTO54aVJc0X41kn1Tyh0M9tuus1xcTLgBbb5Ll37HNOANWF5zYuZwwlU6xNus/apySBWgkOA5xZsoCo8n6lKlecWbKC4W+uYr8OtmfCRZQ1qwKjNycSRgWy+S+c+xzTg20RCPwRmImFA2kxrt1lD6Is0yFfMtQ2d/FbUL63OhQW8P/HrMc+zuRrOC+Tnye3JhlkymTGIEwmzVxA6/siMpVuTbTZJp1qC41y6gHE9DX4yvOhzTLdcZA6yAJLFAttmnYBANpsEmctNOoELzG6PHMzyocTWiZ5mnJwA58nSrS5Lt1FPvsuUTt94O8a9GDnY/7JQc9Wk3aHfWRI8wAJIWnF61nAmfPmm26gn32VCp28iQ2fTMVtxGrFO9DSSbIdxfawzOTg8+VvE6PTdTDtOrXgwPT4DWdJxHSTWiZ4B3OizCFybdZbybBZ3lE7fcm3MryovPaJW6/jzOikTalEZwpqw0kgm9FmY6DybxV2rSWcz7bi98jperj7d3ed1Ujou45uhrAaSRtyeAJdp6msSClrTnacj4iKGkZ868TWiNWIHeiRelg+dDRILIF5xYOx9IMfZB1R9TUIQrDVEIPZw5MKmeQyd/JZrf++0HAad5UNng8Q60b3g1BKeJm71DTgAHB+MkKpos7jzcgUUKqu/+h91emZ3IGePm8CJ1YlufSBeyJSx92mkviahIE6gjDYcuVnjRkcED4jeP5HK3CAbBm1SYU1YXrBRI55rqGkmiM02tUfE9Zj4WtTjIgOdE6O3bCSeSZavNRARuUBEVorIahGZGGX/WSJSKiKLwj93xXtuoGT4qBG3lodNRX2TJNNlAmU8o+6ycg0OL7mZCj4D+BZARCQXeBgYDvQBxohInyiHvquqA8M/9yR4bjC4kU4hIB/soK6pXV/TTLo028QT6ILYHJcxsnyxqHj42YQ1BFgdXt8cEXkGGAV86vK53nN61IjbaxwkIMgZfetrmkmHZpt4Rt2l5SiqBgRmiLXby09nAD8DSGcgMh9BCXBylONOFZFPgI3A/6rqsgTORUTGAeMAunbt6kCxk+RkCvcAfbBTvQIOzJdFQDUU6DJtbpAv66rHYn2XDfKzD0SibKs9pvhjoJuqDgD+ALyYwLmhjapTVbVYVYvbtWuXdGEDJckPtht9FanMjg9q81c6SZfmuHgFqk8nw/suneBnDaQE6BJxv4hQLeMwVd0TcXuGiDwiIm3jOTejJbHGgVtXdqlcAQe5+SudpENzXLwC1adjM94b5GcNZB7QU0R6iEhj4HLg5cgDRKSDiEj49hBC5d0Rz7kZLYlOebeu7FK5Ag7Ul4UJBD/yvcWsmVsq+Ab5VgNR1UMiMgGYCeQC01R1mYiMD+9/FPg2cIOIHALKgcs1NHU+6rm+vBA/JNEp7+aXdbJXwJnYAWxS43WfToM184AsPx1Uvk4kVNUZwIxa2x6NuP0Q8FC852aVBD/YQfyyzrQOYJM6r/O9WTNqamwmepYI4pe1JYc00XjZp2PNqKmxAJIlgvplnUkdwCb9BLFmnk4sgGQR+7JOnc1bySxBrJmnEwsgxsQpUJPcjCOCWjNPFxZAjImTdbhmJquZJ8/WAzEmTtbhasyRrAZi0oqffRDW4Wpc4cBy136xGohJG37nzkqXdURMGknzlPEWQEza8DvRXqYlLjQBkObLXVsTVsDZsNGvBKEPwjpcjaPSPGW81UACzO8mm6DxI9GeMa5K85TxFkAa4uPSsX432STK7bXRrQ/CZBw3lrv2kDVh1cfnpWOD0GQTLy8m2dmkL5NxnF7u2mMWQOqTzNKxDg7JS6dho15NsrM+CJNx0jhlvDVh1SfRDi6Hh+SlU5NNOtWWjDHOsABSn0Q7uBwekpdOw0atg9tkLR/7Sf1mTVj1SXRNZBeG5KVLk41lNTVuCfRQdp/7Sf3maw1ERC4QkZUislpEJkbZf6WILA7//EdEBkTsWyciS0RkkYjMd6WAia6JnOZD8lKRTrUlkz4CP5Q9zScCpsq3GoiI5AIPA98ASoB5IvKyqn4acdha4ExV3SUiw4GpwMkR+89W1e2uFjSRDq5EaywZJl1qSyZ9BD4DcppPBEyVnzWQIcBqVV2jqgeBZ4BRkQeo6n9UdVf47gdAsC/lE62xGGPqFfjBGVnc6gAN1EBE5HxCX9pvquq6iO3/rarTUnzuzsD6iPslHFm7qO1a4PWI+wrMEhEF/qSqU1MsjzPSeEieMUET+KHsWd7qELMGIiK/Au4E+gFvisj3I3ZPcOC5Jco2jVGWswkFkNsjNg9V1ROB4cBNIjIsxrnjRGS+iMzftm1bqmXOKm7PLDemIYEfyp7lrQ711UBGAINU9ZCITAKeFpFjVPVWon/5J6oE6BJxvwjYWPsgEekPPA4MV9UdNdtVdWP491YReYFQk9ic2ueHayZTAYqLi6MGqEyXzCgWW77VBEFaZB/I4laH+gJII1U9BKCqu0VkBDBVRP4JNHbguecBPUWkB7ABuBy4IvIAEekKPA9craqfRWxvBuSo6t7w7fOA7Bj2kKBkA0HgOy9N1rDBGcFVXyf65yJyZs0dVa1S1WuBlUDvVJ84HJwmADOB5cB0VV0mIuNFZHz4sLuANsAjtYbrtgfeE5FPgI+A11T1X6mWKRMlm5Ax8J2Xxhjf1VcDuTTaRlX9iYj80YknV9UZwIxa2x6NuH0dcF2U89YAA2pvN3UlGwgC33lpjPFdzBqIqpararmIvBll959dLJNxULIpRoLaeWkd+8YER8waiIjkA02BtiLSiq86zo8COnlQNuOAZFOMBLHz0jr2TSyBTneSweprwroeuIVQsFjAVwFkD6EZ5CYNpBIIgtZ5aR37Jhq7sPBPzACiqg8CD4rI91X1Dx6WyTgsaIEgWdaxb6KxCwv/xJPKZLOItAAQkZ+IyPMicqLL5TKmDksZb6KxCwv/xBNAfhqeb3E6cD7wFODIKCxjEhHUjn3jL7uw8E88AaSmbngR8EdVfQlnJhIakxBLGW+isQsL/8STzn2DiPwJOBf4tYg0wVYy9NTyTXuYOmcNPx/dl+ZNsnsNsEzpzzHOCeKIwUBZPD20PklpSShL8Dl3OZZ6JZ5vo8uAC4AHwilNOgK3OfLsJi77DhzihYUbGHZcWy4elB1poo1JhF1YxODyiokN1iRUdT+wFTg9vOkQsCrlZzZxO7FrKzoXFvDiwjq5Jo0xmcqJtdZdXjGxwQAiIncTSqN+R3hTHvBXR57dxCUnRxg1sBPvrd7O9rIDfhfHGOO2mppD6XpAv6o5JBpEXF4xMZ6+jIuBkcA+OJxGvYUjz27iNmpgZ6qqlRlLNvldFGOM25yqObi8YmI8AeSgqirhxZ7C6dONx3p1aMHxHVpY7idjsoFTNYdz7gqtkBjJwRUT4wkg08OjsApF5HvAbOAxR57dJGTUwM58/OVuvtyx3++iGGPc5FTNweUVE+MZhdUOeJZQDqxehNboONeRZ89WSQ6rGzGgI7/+1wpeWbyRm87+mgcFjc4S1xnjMifXWndxxcR4Asg3VPV24I2aDSLyG45cn9zEK4VhdUWtmjK4eyteWLiBG886FhEnVhZOjBOJ6ywAGdOAmu8Cl+ZvOCVmE5aI3CAiS4BeIrI44mctsNi7ImaYFDvHLj2pC6u3ljF3zY6GD3ZBsisc1qgJQBt2l6N8FYCsb8eYWvpfBrcuhUm7Q78DFjyg/hrI08DrwL3AxIjte1V1p6ulymQpdo6NHNiJX/9rBb94dTml5ZWeX8WnmrjOMqdmD6tpJsjFGeNuqW9FwlJVXaeqY1T1i4gfx4KHiFwgIitFZLWITIyyX0RkSnj/4sgswA2dG1gpdo7l5+UyuEdrPt20x5er+FQT11nm1OxgNc0EOTXvw2O+5bQSkVxCC1MNB/oAY0SkT63DhgM9wz/jCGcBjvPcYHJgWN0nX+6usy2RZqRUpJq4zjKnZodUmzqzjsszxt3iZ1LEIcBqVV2jqgeBZ4BRtY4ZBfxZQz4gNJS4Y5znBpMDw+o276mIut2Lq/hUM+ImG4BsLfT0YjXNBLk8Y9wtfqZ27Qysj7hfApwcxzGd4zwXABEZR6j2QteuXVMrsVNSHFbXqbCADVH+Eb26ik8lcV0ymVNtydL04/lnNA37D47QsijcfBVle4D5WQOJNgZV4zwmnnNDG1Wnqmqxqha3a9cuwSIGU7qvfzB6UGfen/h11k6+iPcnfr3BIGDNIenH089omvYfHMHlGeNu8TOAlABdIu4XAbXTzcY6Jp5zM1ZNM1KnlvkA5Aj85KLeGXs1bs0h6cfTxb/StP/gCC7PGHeLn01Y84CeItID2ABcDlxR65iXgQki8gyhJqpSVd0kItviODej1TQjLdtYysiH3ueTkt1cSTe/i5WYOJsd/G6yM8nxbI2ONO0/qMPFGeNu8a0GoqqHgAnATGA5MF1Vl4nIeBEZHz5sBrAGWE0o/9aN9Z3r8UvwXpT1AU7o1JJxw45h+vwS3l+93e8Sxi+BZod0b7IzLnM546yJTUKJdrNDcXGxzp8/3+9iJKd2ChQItZGOmEJF728x/MF3qapWZt4yjILGubEfJyh+1zdGp2GX0KzbWmxSmompnv+NdLuiDyoRWaCqxXW2WwDxgBMjRBr4wv1gzQ4un/oB3zujB3delAZTYiYVEn3cg4RSNxiTiCCOwgpimZIUK4D42QeSHZxak7iBdt5TjmnDmCFdeeK9tXyzfycGdClModAeSNNhiyaggtZ/4PJa5EHh5yis7ODhymJ3XHg87Vo04fbnFlNZVZ1gQT2WpsMWjYlLJowMi4MFELd5uLLYUfl5/HxUX1Zs3suf3vk8wYJ6LE2HLRoTl0wZGdYAa8Jym1NNNXGuD3DeCR24qF9Hpry5mgv6duRrRzdPsuAeCFqzgzFOyZImWquBuM3Jppo41weYNPIEChrncsfzi6mqjnOQRJQhwsaYJGVJE60FELf50FTTrkUT7vpmH+at28VvZsWR7iMTUkEYQ4CSbmZJE60N481QqsqPX1jK3z/6kj+MGcSIAZ1iH5zgnAxjgqh20k0ITTh1LYVKFok1jNdqIBlKRPjZyBMo7taK2579hKUbSmMfnCUdfl4LzNVwlrCkm96zAJLBGjfK4Y9XnUSrpo25/i8L2FF2IPqBlgrCcbYin/cs6ab3LIBkuHYtmjD16mK2lx1g/F8XUFHrCg3Img6/hjhZY7Cr4SN5UQ41WpEAABoaSURBVBtLdbVLqzEmzgJIFuhX1JIHLh3AvHW7+J/pn1Bde2SWhx1+Qf0ndbrGYFfDX/GqNpZK0k2rMSbH5oFkiREDOrG5tIJfzlhOuxZNuHtEH0Qi1uXyYE5GkFcWrK/GkEzZLAX9V5x+b2NJZrVLr8uYaSyABInLydeuO6MHm/dU8MR7aylsmsct5x7n2GPHI9F/Ui8z8DpdY7jt/F5RRwRlYwp6L2tjya5B4mmNMYOSLFoACQoPkq+JCHde2Js95ZX8fvYqmjTK5YazjnXkseORyD+p17UVp2sMqVwNZ5p0qI15VsYMS7JofSBB4VHytZwcYfK3+jNqYCd+/a8VPP7uGkcfvz6JdHJ63QntxqJVia797gU/+qDSYUEwz8qYYUkWrQYSFB7OxcjNEX5z6QAOHqrmF68tJzdH+O7QHo4/T22JNOt43QmdDTUGv/qg0uG99ayMGTbnypcAIiKtgX8A3YF1wGWquqvWMV2APwMdgGpgqqo+GN43CfgesC18+I9VdYYXZXeNx8nXGuXm8ODlg6j++8f87JVPOXiomuvPdLc5K5F/Uj+aPTxbw9snfnYUe/Heptpn5snfP8OSLPrVhDUReFNVewJvhu/Xdgj4H1XtDZwC3CQikUvt/U5VB4Z/ghc8Ek1O6MNcjMaNcnjoihMZMaAT976+ggdmriTu1DZJJl+Mt1knHZo90k0mDy1Om2G4GTbnyq8AMgp4Knz7KWB07QNUdZOqfhy+vRdYDqTH5WEyyQl9Sr6Wl5vD778zkDFDuvDQ26v5yYtLOdTQYlQeJF8cPagz917Sj86FBQjQubDAchqlKNWJdkGWNhM3MyzJoi/JFEVkt6oWRtzfpaqt6jm+OzAH6Kuqe8JNWNcAe4D5hGoqu2KcOw4YB9C1a9eTvvjiC4deRT3SMDmhqnLfzJX88d+f8/Xjj+YPYwbRrMlXLZyRzQNz839Ah8OthxEC9vq8HAacDjI52WCPia8R7ZtMgLWTL2JzaQXtWjQhN0eiHGUa4nkyRRGZLSJLo/yMSvBxmgPPAbeo6p7w5j8CxwIDgU3Ab2Kdr6pTVbVYVYvbtWuX5KtJUBp2lIkIt19wPD8f3Zd/r9zKZX+ay+bSCqBu88DRGiV4QKBeX9o0aXgok2t19dWu9h88xBWPf8At/1jkcakyn2ud6Kp6bqx9IrJFRDqq6iYR6QhsjXFcHqHg8TdVfT7isbdEHPMY8KpzJXdAGneUXX1KN4paFTDhbx8z+uH3mXbN4DrNAxu1LUWyve7JAXp9NrM4ukwdKFDfCL+fvfwpa7fv4xej+vpYwszkVx/Iy8DY8O2xwEu1D5BQno0ngOWq+tta+zpG3L0YCE67CaRPR1mMjvCzex3NP8efhghc+uh/6oyGuu/QZezXxkc+VsBeXyZ3GJu6YtWucnOEf8xfz41nHctpX2vrdzEzjl/zQCYD00XkWuBL4FIAEekEPK6qFwJDgauBJSJSU/esGa57n4gMBJTQMODrPS5//eJcv9xXDcyI7dPpKF68aSjXPjWPpRv2HHHqy9WnQyX8uPE/6cD2QL6+dJj9bJxVu3a1fud+LnzwXQZ1LfQ8bU+2sBUJs1WcHf3lB6u4+okPmf/FkWMUgt75mskdxqZhh6qquexPc1m1pYwZPziDLq2b+l2ktGYrEpojxdnRX9A4l3+OP5VLIr502zVvEvgv4kzuMDYNe/DNVXz85W5+dUk/Cx4uslQm2SqBjn4R4bffGciVp3RlwtML2VF2kNLySlT1yJTwAZOpHcamfu+v3s5Db6/msuIiRgzo5HdxMprVQLJVEh39J3VrzWs3n8HQr7Xh7peX8b0/17NMrjE+WL21jBv/9jFfa9ecSSNP8Ls4Gc8CSLZKckZs62aNeWLsYH76zT7M+WwbFzz4Lm+t2FLvOcZ4YcueCsZO+4i8XGHaNYNp2tgaWNxmnegmaZ9u3MMPpy9ixea9jBnShTsv6kPzJgH4p82gBXtMfHbuO8jlU+eyYVc5z4w7lX5FLf0rTAZ+/qwT3TiuT6ejeGnCUK4fdgzPzFvP+b+bwzufxZil7hUP8nSZYNm57yBXPPYBX+zYz2Nji/0PHln0+bMAYlLSpFEud1zYm2fHn0p+Xg5jp33Erf9YxNY9Ff4UKMMW7DH127b3AFc89gFrt+/j8bHFnHasz5MFs+zzF4D2BpMJajrYH3prNVPnrGHWss3cfE5Pvju0B40beXidUs/wZDeSK1rCRv+s37mfq5/4kM17Knh8bDFn9PQo11190jAPXiqsBmLi18AaIPl5ufzv+b2YdeswTj6mDfe+voILHpzDHC+btWLk49pf0MHx5IpBStjox1K1flq6oZRvP/ofdu47yN+uOzkYwQNi54MLUJ44J1kASUdJLuaU8nPG2bbbvW0zpl0zmGnXFFNdrfzXtI8Y9+f5rN+53/1yxhiefF/ldxxfLyIoa1AEKZDV4cJndfanW7jsT3PJFeGf40/jpG6tHSioQ9IlD55DLICkG7866ZJo2/368e2Zeeswbju/F++u2s65v32H377xGeUHq2Kek7IYw5OfKhsS9fBUkisGJWFjUAJZHQ5/Vqurld/P/ozr/jyfY9s158WbhtKrQwtny5yqDFswqiHWB5Ju6vsid/NDmmTbbpNGudx09te45MTO/GrGCqa8uYrnFpRw50W9Gd63gzsz2ftfVue96DTjLceTKwYlYWNQAlkdDn5WS8sr+eE/FvHmiq2hz9LF/civteRxYET5/GUqq4GkG7866VJs2+3YsoA/jBnEM+NOoUV+I24Mrzfy3qoo64o4KdyE8l7FJbzf5GZG5rx3eFeqa6wHZd32wC5V69Bn9T+rtzP896Eh4j8fdQK/uXRAcINHlrEAkm786qRzqG33lGPa8NrNZ3Dft/qzbe8BrnriQy6fOpcP1+xwsLBhEU0ogtJZtvPrxk8wKuc9R5IrBiVhY1ACWR0pflYrKqu455VPueLxD8nPy+XZG07j6lO7Bzr/WraxmejppvY6HhD6IveindXhGbYVlVX8/aMveeTfn7Nt7wFO7tGaH5zTk1OPbePMl0Qark2frEAOJ07hs7p0Qym3/GMRq7eWMfbUbkwc3puCxlbr8EusmegWQNJRhqVKqKis4ukPv+TRdz5n694DDOhSyPhhx3DeCR3IzUkhkEwqJLTmWG0Ck3Yn/7gmfgl+VvcfPMSDb67iiXfX0qZ5Y+7/9gCGHReQIbpZzAIIGRRAMlRFZRX/XFDC4++u4Ysd++nRthnfO+MYLjmxc3Jt3llUA0l3qsrMZVu455VlbCyt4LLiIn58YW8KmzZu+GTjukAFEBFpDfwD6E5oSdrLVHVXlOPWAXuBKuBQzQuI9/zaLICkh6pqZeayzTz6zucsLimlTbPGXHFyV648uRsdWubH/0B+NveZuC3dUMovX1vO3DU7OL5DC34xui/F3QM0t8MELoDcB+xU1ckiMhFopaq3RzluHVCsqtuTOb82CyDpRVWZu2YH095bx5srtpArwvl9O3DVyd045ZjW8fWTZFhzXyZZt30fv5v9GS8t2kirpnnc+o3jGDOkK3m5NrYnaIIWQFYCZ6nqJhHpCPxbVesMGakngMR1fm0WQNLXlzv28+e565g+fz17Kg5xTNtmfLu4iG+dWET7oxKolQRUIDvBXfL5tjIefms1Ly7aQONGOXx3aA/Gn3ksLQvy/C6aiSFoAWS3qhZG3N+lqq2iHLcW2EWoJ/RPqjo1kfNrswCS/soPVvHakk1Mn7+ej9buJEfgrF5Hc+lJRZzTu723iRsdUpOKJHI2eUFebsat4b5sYyl/emcNryzeSJNGOVx1cjfGDTuGo4/KT/8AmuE1Xc8DiIjMBjpE2XUn8FScAaSTqm4UkaOBN4Dvq+qcRAKIiIwDxgF07dr1pC+++CK1F2YCY+32fTy7YD3PLihhy54DtGqax0X9OzJqYGdO6tqKnFRGcHlo6OTos+Q7Fxbw/sSv+1Ai5zy/oIRfzFjOzn0HAWgSrnFcd0YP2jZvArgfQF0PTlnQ1xa0GkjCTVAiMgkoU9UHrAnLRKqqVuas2sZzC0qYvXwLFZXVdGyZz4X9OjK8bwdODHgw6THxtViDjVk7+SKvi+OIzaUV3PPKMl5fuvmI15bfKIfJ3+p/xBe4mwHUk9pdFoz2ixVA/MqF9TIwFpgc/v1S7QNEpBmQo6p7w7fPA+6J93yTPXJzhLN7Hc3ZvY6m7MAhZn+6hVcXb+Qvc7/giffWcnSLJpzbpz3f6N2eU49tE7g0GEHJqZWq0vJK3gi/93M+20Z1lKhYcaia+2euPOLL281cXvUlmnQsgGTZGiCR/Aogk4HpInIt8CVwKYSarIDHVfVCoD3wQnikTSPgaVX9V33nG9O8SSNGD+rM6EGd2VtRyZvLtzJz2WZeXLiBpz/8kqaNcznt2Lac1asdZx7Xji6tm/pdZG47v1fUq2TfU5HUEq0p6Ou9j2b2p1t4bfEm5qzaRmWV0rmwgOvPPJY//vvzqI9TOzC4GUAdD07R+jpaFsWogWTmGiCRfAkgqroDOCfK9o3AheHba4ABiZxvTKQW+XmHg0lFZRUfrNnBm8u38taKrcxevgWAHm2bcdqxbRj6tbYM6dH6cLu8l2quhIPciVy7KWjD7nJ+OH0RIkJVtdKpZT7XnNadi/p3YkBRS0SElxdtjCswuBlAHQ1Otfs6atLTD7gCPnm6bh9Ihq4BEslmopuso6p8vm0fcz7bxnurt/Phmh3sC69R0vPo5gzu0ZrinFUUf/Z7upQtRgo9HlXj94ieWs9fNuwuzpzRih3hjvBIzZrk8pdrT2ZQl8I683IS6X9wq6Pb0T6Q+vo6zrnLRmFlOgsgJprKqmqWbCjlgzU7+GDNTj5es5WyQ6Evw1bspW/OWvo2Wk//IefQ97ThFLUqcC8jrN8jehZPZ8tLd/HJwU58XN2TD6p7s0SPoYro/UYNdfQ3GBg8CJaOBacszq1mAQQLICY+Vb/tx2e7lQXVx7FEj2FJdQ8+0yIOhVt8C5vm0afjURzXvgU92zfnuPYt6NG2GW2aNU49sMQxosepL8RDVdV8sXM/q7bsZeXmMpZs2M3ilavZWn0UAI04xED5nFNyPuW4nI3cXHlTncdIaaSU38EyUVkw2iqWoI3CMiawcvesp3eO0jtnPfAmABWax0rtypILX2DphlKWb9rD9Pnr2R+xPG+zxrl0a9OMbm2a0rVNU7q1bkanwnw6tiygXYsmtGqa13CAaWBET7S+iDueXwJQJ4hUVFaxdc8BNu+pYEvET8muctZu38ea7fs4eKgaABE4tl1zTucT+jVaQ/+cNfSRLyiQULOVItzOzc72U/i1umayzrkresDLgr6OWCyAGFNblFE1+VLJgMKDDDil2+Ft1dXKxtJyVm0pY92OfXyxYz9f7NjHyi17mb18C5VVR9bu83KFts2b0KZ5Y1o1bUxh08a0yG9E8yaNaNa4Ec3zG9E07xLyKzaTxyEaUUUOSi7VVDdtR/WyzdzzyqdRh6Xe+cIS3l21ne1lB9iyp4LNeyrYvb+yzkvLz8uhU8sCurdtxrDj2nFc+xYc1745Xzu6OU0bN4Lf3RT1KltaFnHvWf2c7adIt+GvNUEtg/s6EmVNWMbU5kDTSlW1snlPBZt2l7NlzwG27q1g694DbN1zgF37D7Jz30F27z9I2YFDlB04REVldcrF7tQyn7YtmtD+qHzaH9WEDkflh2/n06FlPu1b5HNUQaP6a0FeNitlcZNQurEmLJN+/BqN5MCVZm6O0LmwgM5xDhc9VFXNvgNV7Dt4iANLX6Vy7qMc2ruN6uYdqBo8jpye3yAnB777f/PYuvdAnfMdS3vi5VW2NQmlPauBmGBKtw5Wj2Rc4kW/hyybuFgNxKSXdOtg9Ug6TDpMSP/Lsvrvme4sgJhgSrcOVg/VzK43xm/pt3iCyQ6x8ghlQX4hY9KFBRATTOfcFerziGQdrMYEigUQE0z9Lwt1mLfsAkjod5Z3oBsTNNYHYoLLOliNCTSrgRhjjEmKBRBjjDFJsQBijDEmKRZAjDHpY/H0UA6tSYWh34un+12irOZLABGR1iLyhoisCv9uFeWYXiKyKOJnj4jcEt43SUQ2ROy70PtXYUyC7MsvNTXpbUrXA/rVkrL2PvrGrxrIROBNVe1JaMGFibUPUNWVqjpQVQcCJwH7gRciDvldzX5VneFJqY1Jln35pa6+9DbGF34FkFHAU+HbTwGjGzj+HOBzVf3C1VIZ45YgfPmlew3I0tsEjl8BpL2qbgII/z66geMvB/5ea9sEEVksItOiNYHVEJFxIjJfROZv27YttVIbkyy/v/wyoQZk6W0Cx7UAIiKzRWRplJ9RCT5OY2Ak8M+IzX8EjgUGApuA38Q6X1Wnqmqxqha3a9cuiVdijAP8/vILQg0oVZbeJnBcm4muqufG2iciW0Sko6puEpGOwNZ6Hmo48LGqbol47MO3ReQx4FUnymyMa/xePMnvGpATbEnZwPErlcnLwFhgcvj3S/UcO4ZazVc1wSd892LA1r80web3l1+Udd4Pb08nlt4mUHxZkVBE2gDTga7Al8ClqrpTRDoBj6vqheHjmgLrgWNUtTTi/L8Qar5SYB1wfURAiclWJDRZy1Z4NCkI1IqEqrqD0Miq2ts3AhdG3N8PtIly3NWuFtCYTON3DchkJMvGa0y2sOYf4zBLZWKMMSYpFkCMMcYkxQKIMcaYpFgAMcYYkxQLIMYYY5LiyzwQv4jINsDLhIxtge0ePl+irHypsfKlxsqXGi/L101V6+SCyqoA4jURmR9t8k1QWPlSY+VLjZUvNUEonzVhGWOMSYoFEGOMMUmxAOKuqX4XoAFWvtRY+VJj5UuN7+WzPhBjjDFJsRqIMcaYpFgAMcYYkxQLICkSkdYi8oaIrAr/rrM+u4j0EpFFET97ROSW8L5JIrIhYt+FdZ/F3fKFj1snIkvCZZif6Plulk9EuojI2yKyXESWicgPIva58v6JyAUislJEVovIxCj7RUSmhPcvFpET4z3Xo/JdGS7XYhH5j4gMiNgX9W/tcfnOEpHSiL/bXfGe61H5boso21IRqRKR1uF9rr5/IjJNRLaKSNSF8vz+7B1BVe0nhR/gPmBi+PZE4NcNHJ8LbCY0MQdgEvC/fpeP0MJcbVN9fW6UD+gInBi+3QL4DOjj1vsX/ht9DhwDNAY+qXm+iGMuBF4HBDgF+DDecz0q32lAq/Dt4TXlq+9v7XH5zgJeTeZcL8pX6/gRwFsevn/DgBOBpTH2+/bZq/1jNZDUjQKeCt9+ChjdwPHnAJ+rqlcz4hMtn9Pnp/z4qrpJVT8O394LLAc6O1yOSEOA1aq6RlUPAs+EyxlpFPBnDfkAKBSRjnGe63r5VPU/qrorfPcDwMu1a1N5DwLx/tVSZ1ltN6nqHGBnPYf4+dk7ggWQ1LXX8HK64d9HN3D85dT9ME4IV0WnOd1ElED5FJglIgtEZFwS57tdPgBEpDswCPgwYrPT719nQksp1yihbsCKdUw853pRvkjXErpirRHrb+11+U4VkU9E5HUROSHBc70oX82y2hcAz0Vsdvv9a4ifn70j2IqEcRCR2UCHKLvuTPBxGgMjgTsiNv8R+DmhD+XPgd8A/+1D+Yaq6kYRORp4Q0RWhK+EUubg+9ec0D/yLaq6J7w55fcv2lNF2VZ7vHusY+I5N1VxP4eInE0ogJwesdm1v3UC5fuYUDNuWbjf6kWgZ5znelG+GiOA91U1skbg9vvXED8/e0ewABIHVT031j4R2SIiHVV1U7gaubWehxoOfKyqWyIe+/BtEXkMeNWP8mloPXpUdauIvECoOjwHSOT1uVY+EckjFDz+pqrPRzx2yu9fFCVAl4j7RcDGOI9pHMe5XpQPEekPPA4MV9UdNdvr+Vt7Vr6ICwBUdYaIPCIibeM514vyRajTYuDB+9cQPz97R7AmrNS9DIwN3x4LvFTPsXXaUsNfmjUuBqKOvEhBg+UTkWYi0qLmNnBeRDkSeX1ulU+AJ4DlqvrbWvvceP/mAT1FpEe41nh5uJy1y/1f4RExpwCl4Sa4eM51vXwi0hV4HrhaVT+L2F7f39rL8nUI/10RkSGEvot2xHOuF+ULl6slcCYRn0mP3r+G+PnZO5KbPfTZ8AO0Ad4EVoV/tw5v7wTMiDiuKaF/kJa1zv8LsARYHP5jd/S6fIRGbXwS/lkG3NnQ+R6X73RCVfHFwKLwz4Vuvn+ERrp8RmhUy53hbeOB8eHbAjwc3r8EKK7vXBc+dw2V73FgV8T7Nb+hv7XH5ZsQfv5PCHXynxak9y98/xrgmVrnuf7+EbrI3ARUEqptXBukz17kj6UyMcYYkxRrwjLGGJMUCyDGGGOSYgHEGGNMUiyAGGOMSYoFEGOMMUmxAGKMC0SkUERuTPLcW8IpNGru/1JE1otImXMlNCZ1FkCMcUchkFQAAW4hNG+oxiuEZjsbEyiWysQYd0wGjhWRRcAbhFK0XAY0AV5Q1bvDM5mnE0o5kUsol1d7QpMo3xaR7ap6toYyrhKeuG1MYFgAMcYdE4G+qjpQRM4Dvk2oFiHAyyIyDGgHbFTViyCUOkNVS0Xkh8DZqrrdr8IbEw9rwjLGfeeFfxYSykJ7PKHMs0uAc0Xk1yJyhqqW+lhGYxJmNRBj3CfAvar6pzo7RE4ilL/oXhGZpar3eF46Y5JkNRBj3LGX0PK7ADOB/w6vZ4KIdBaRo0WkE7BfVf8KPEBoGdPa5xoTWBZAjHGBhtbfeF9ElgLfAJ4G5orIEuBZQgGiH/BRuKP9TuAX4dOnAq+LyNsAInKfiJQATUWkREQmeftqjInOsvEaY4xJitVAjDHGJMUCiDHGmKRYADHGGJMUCyDGGGOSYgHEGGNMUiyAGGOMSYoFEGOMMUn5f3i7MQNj3h1EAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 求解边界\n",
    "\n",
    "X1_new = X1.sort_values()\n",
    "theta0 = LR.intercept_\n",
    "theta1, theta2, theta3, theta4, theta5 = LR.coef_[0][0], LR.coef_[0][1], LR.coef_[0][2], LR.coef_[0][3], LR.coef_[0][4]\n",
    "a = theta4\n",
    "b = theta5 * X1_new + theta2\n",
    "c = theta0 + theta1 * X1_new + theta3 * X1_new * X1_new\n",
    "X2_new_boundary = (-b + np.sqrt(b * b - 4 * a * c))/(2 * a)\n",
    "fig2 = plt.figure()\n",
    "passed = plt.scatter(data.loc[:, 'test1'][mask], data.loc[:, 'test2'][mask])\n",
    "failed = plt.scatter(data.loc[:, 'test1'][~mask], data.loc[:, 'test2'][~mask])\n",
    "plt.plot(X1_new, X2_new_boundary)\n",
    "plt.title('test1-test2')\n",
    "plt.xlabel('test1')\n",
    "plt.ylabel('test2')\n",
    "plt.legend((passed, failed), ('passed', 'failed'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义f(x)\n",
    "def f(x):\n",
    "    a = theta4\n",
    "    b = theta5 * x + theta2\n",
    "    c = theta0 + theta1 * x + theta3 * x * x\n",
    "    X2_new_boundary1 = (-b + np.sqrt(b * b - 4 * a * c))/(2 * a)\n",
    "    X2_new_boundary2 = (-b - np.sqrt(b * b - 4 * a * c))/(2 * a)\n",
    "    return X2_new_boundary1, X2_new_boundary2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
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    }
   ],
   "source": [
    "X2_new_boundary1 = []\n",
    "X2_new_boundary2 = []\n",
    "for x in X1_new:\n",
    "    X2_new_boundary1.append(f(x)[0])\n",
    "    X2_new_boundary2.append(f(x)[1])\n",
    "print(X2_new_boundary1, X2_new_boundary2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig3 = plt.figure()\n",
    "passed = plt.scatter(data.loc[:, 'test1'][mask], data.loc[:, 'test2'][mask])\n",
    "failed = plt.scatter(data.loc[:, 'test1'][~mask], data.loc[:, 'test2'][~mask])\n",
    "plt.plot(X1_new, X2_new_boundary1)\n",
    "plt.plot(X1_new, X2_new_boundary2)\n",
    "plt.title('test1-test2')\n",
    "plt.xlabel('test1')\n",
    "plt.ylabel('test2')\n",
    "plt.legend((passed, failed), ('passed', 'failed'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 两段缝合\n",
    "# 设置更加密集的X轴间隔\n",
    "X1_range = [-0.9 + x/10000 for x in range(0, 20000)]\n",
    "\n",
    "X2_new_boundary1 = []\n",
    "X2_new_boundary2 = []\n",
    "for x in X1_range:\n",
    "    X2_new_boundary1.append(f(x)[0])\n",
    "    X2_new_boundary2.append(f(x)[1])\n",
    "\n",
    "fig3 = plt.figure()\n",
    "passed = plt.scatter(data.loc[:, 'test1'][mask], data.loc[:, 'test2'][mask])\n",
    "failed = plt.scatter(data.loc[:, 'test1'][~mask], data.loc[:, 'test2'][~mask])\n",
    "plt.plot(X1_range, X2_new_boundary1)\n",
    "plt.plot(X1_range, X2_new_boundary2)\n",
    "plt.title('test1-test2')\n",
    "plt.xlabel('test1')\n",
    "plt.ylabel('test2')\n",
    "plt.legend((passed, failed), ('passed', 'failed'))\n",
    "plt.show()"
   ]
  }
 ]
}